Choose a pricing tier for Azure Search
In Azure Search, a resource is created at a pricing tier or SKU that is fixed for the lifetime of the service. Tiers include Free, Basic, or Standard, where Standard is available in several configurations and capacities. Most customers start with the Free tier for evaluation and then graduate to Standard for development and production deployments. You can complete all quickstarts and tutorials on the Free tier, including those for resource-intensive cognitive search.
Tiers reflect the characteristics of the hardware hosting the service (rather than features) and are differentiated by:
- Number of indexes you can create
- Size and speed of partitions (physical storage)
Although all tiers, including the Free tier, generally offer feature parity, larger workloads can dictate requirements for higher tiers. For example, cognitive search indexing has long-running skills that time out on a free service unless the data set happens to be small.
The exception to feature parity is indexers, which are not available on S3HD.
Within a tier, you can adjust replica and partition resources to increase or decrease scale. You could start with one or two of each, and then temporarily raise your computational power for a heavy indexing workload. The ability to tune resource levels within a tier adds flexibility, but also slightly complicates your analysis. You might have to experiment to see whether a lower tier with higher resources/replicas offers better value and performance than a higher tier with lower resourcing. To learn more about when and why you would adjust capacity, see Performance and optimization considerations.
Tiers for Azure Search
|Free||Shared with other subscribers. Non-scalable, limited to 3 indexes and 50 MB storage.|
|Basic||Dedicated computing resources for production workloads at a smaller scale. One 2 GB partition and up to three replicas.|
|Standard 1 (S1)||From S1 on up, dedicated machines with more storage and processing capacity at every level. Partition size is 25 GB/partition (max 300 GB documents per service) for S1.|
|Standard 2 (S2)||Similar to S1 but with 100 GB/partitions (max 1.2 TB documents per service)|
|Standard 3 (S3)||200 GB/partition (max 2.4 TB documents per service).|
|Standard 3 High-density (S3-HD)||High density is a hosting mode for S3. The underlying hardware is optimized for a large number of smaller indexes, intended for multitenancy scenarios. S3-HD has the same per-unit charge as S3 but the hardware is optimized for fast file reads on a large number of smaller indexes.|
How billing works
In Azure Search, there are three ways to incur costs in Aure Search, and there are fixed and variable components. This section looks at each billing component in turn.
1. Core service costs (fixed and variable)
For the service itself, the minimum charge is the first search unit (1 replica x 1 partition), and this amount is constant for the lifetime of the service because the service cannot run on anything less than this configuration.
In the following screenshot, per unit pricing is indicated for Free, Basic, and S1 (S2 and S3 are not shown). If you created a basic service or a standard service, your monthly cost would average the value that appears for price-1 and price-2 respectively. Unit costs go up for each tier because the computational power and storage capacity is greater at each consecutive tiers.
Additional replicas and partitions are an add-on to the initial charge. A search service requires a replica and partition so the minimum configuration is one of each. Beyond the minimum, you add replicas and partitions independently. For example, you could add only replicas or only partitions.
Additional replicas and partitions are charged based on a formula. The costs are not linear (doubling capacity more than doubles the cost). For an example of how of the formula works, see "How to allocate replicas and partitions"
2. Data egress charges during indexing
When pulling data from an Azure SQL Database or Cosmos DB data source, you will see charges for the transaction in the bill for those resources. Those charges are not Azure Search meters, but they are mentioned here because if you are using indexers to pull data from Azure SQL Database or Azure Cosmos DB, you'll see that charge in your bill.
3. AI-enriched indexing using Cognitive Services
For cognitive search only, image extraction during document cracking is billed based on the number of images extracted from your documents. Text extraction is currently free. Other enrichments based on built-in cognitive skills are billed against a Cognitive Services resource. Enrichments are billed at the same rate as if you had performed the task using Cognitive Services directly.
Billing based on search units
For Azure Search operations, the most important billing concept to understand is a search unit (SU). Because Azure Search depends on both replicas and partitions for indexing and queries, it doesn't make sense to bill by just one or the other. Instead, billing is based on a composite of both.
SU is the product of replica and partitions used by a service:
(R X P = SU)
Every service starts with one SU (one replica multiplied by one partition) as the minimum. The maximum for any service is 36 SUs, which can be achieved in multiple ways: 6 partitions x 6 replicas, or 3 partitions x 12 replicas, to name a few. It's common to use less than total capacity. For example, a 3-replica, 3-partition service, billed as 9 SUs. You can review this chart to see valid combinations at a glance.
The billing rate is hourly per SU, with each tier having a progressively higher rate. Higher tiers come with larger and speedier partitions, contributing to an overall higher hourly rate for that tier. Rates for each tier can be found on Pricing Details.
Most customers bring just a portion of total capacity online, holding the rest in reserve. In terms of billing, it's the number of partitions and replicas that you bring online, calculated using the SU formula, that determines what you actually pay on an hourly basis.
Billing for image extraction in cognitive search
If you are extracting images from files in a cognitive search indexing pipeline, you are charged for that operation in your Azure Search bill. The parameter that triggers image extraction is imageAction in an indexer configuration. If imageAction is set to none (default), there are no charges for image extraction.
Pricing is subject to change, but is always documented on the Pricing Details page for Azure Search.
Billing for built-in skills in cognitive search
When you set up an enrichment pipeline, any built-in skills used in the pipeline are based on machine learning models. Those models are provided by Cognitive Services. Usage of those models during indexing is billed at the same rate as if you had requested the resource directly.
For example, assume a pipeline consisting of optical character recognition (OCR) against scanned image JPEG files, where the resulting text is pushed into an Azure Search index for free-form search queries. Your indexing pipeline would include an indexer with the OCR skill, and that skill would be attached to a Cognitive Services resource. When you run the indexer, charges appear on your Cognitive Resources bill for OCR execution.
Tips for reducing costs
You cannot shut down the service to lower the bill. Dedicated resources are operational 24-7, allocated for your exclusive use, for the lifetime of your service. The only way to lower a bill is by reducing replicas and partitions to a low level that still provides acceptable performance and SLA compliance.
One lever for reducing costs is choosing a tier with a lower hourly rate. S1 hourly rates are lower than S2 or S3 rates. Assuming that you provision a service aimed at the lower end of your load projections, if you outgrow the service, you could create a second larger-tiered service, rebuild your indexes on that second service, and then delete the first one.
If you have done capacity planning for on premises servers, you know that it's common to "buy up" so that you can handle projected growth. But with a cloud service, you can pursue cost savings more aggressively because you are not locked in to a specific purchase. You can always switch to a higher-tiered service if the current one is insufficient.
In Azure Search, capacity is structured as replicas and partitions.
Replicas are instances of the search service, where each replica hosts one load-balanced copy of an index. For example, a service with 6 replicas has 6 copies of every index loaded in the service.
Partitions store indexes and automatically split searchable data: two partitions split your index in half, three partitions into thirds, and so forth. In terms of capacity, partition size is the primary differentiating feature across tiers.
All Standard tiers support flexible combinations replica and partitions so that you can weight your system for speed or storage by changing the balance. Basic offers up three replicas for high availability but has only one partition. Free tiers do not provide dedicated resources: computing resources are shared by multiple subscribers.
More about service limits
Services host resources, such as indexes, indexers, and so forth. Each tier imposes service limits on the quantity of resources you can create. As such, a cap on the number of indexes (and other objects) is the second differentiating feature across tiers. As you review each option in the portal, note the limits on number of indexes. Other resources, such as indexers, data sources, and skillsets, are pegged to index limits.
Most customers start with the Free service, which they keep indefinitely, and then choose one of the Standard tiers for serious development or production workloads.
On either end, Basic and S3 HD exist for important but atypical consumption patterns. Basic is for small production workloads: it offers SLA, dedicated resources, high availability, but modest storage, topping out at 2 GB total. This tier was engineered for customers who consistently under utilized available capacity. At the far end, S3 HD is for workloads typical of ISVs, partners, multitenant solutions, or any configuration calling for a large number of small indexes. It's often self-evident when Basic or S3 HD tier is the right fit, but if you want confirmation you can post to StackOverflow or contact Azure Support for further guidance.
Shifting focus to the more commonly used standard tiers, S1-S3 are a progression of increasing levels of capacity, with inflection points on partition size and maximums on numbers of indexes, indexers, and corollary resources:
|partition size||25 GB||100 GB||200 GB|
|index and indexer limits||50||200||200|
S1 is a common choice when dedicated resources and multiple partitions become a necessity. With partitions of 25 GB for up to 12 partitions, the per-service limit on S1 is 300 GB total if you maximize partitions over replicas (see Allocate partitions and replicas for more balanced compositions.)
Portal and pricing pages put the focus on partition size and storage, but for each tier, all compute capabilities (disk capacity, speed, CPUs) grow linearly with price. An S2 replica is faster than S1, and S3 is faster than S2. S3 tiers break the generally linear compute-pricing pattern with disproportionately faster I/O. If you anticipate I/O as the bottleneck, an S3 gives you much more IOPS than lower tiers.
S3 and S3 HD are backed by identical high capacity infrastructure but each one reaches its maximum limit in different ways. S3 targets a smaller number of very large indexes. As such, its maximum limit is resource-bound (2.4 TB for each service). S3 HD targets a large number of very small indexes. At 1,000 indexes, S3 HD reaches its limits in the form of index constraints. If you are an S3 HD customer who requires more than 1,000 indexes, contact Microsoft Support for information on how to proceed.
Previously, document limits were a consideration but are no longer applicable for new services. For more information about conditions under which document limits still apply, see Service limits: document limits.
Capacity and costs of running the service go hand-in-hand. Tiers impose limits on two levels (storage and resources), so you should think about both because whichever one you reach first is the effective limit.
Business requirements typically dictate the number of indexes you will need. For example, a global index for a large repository of documents, or perhaps multiple indexes based on region, application, or business niche.
To determine the size of an index, you have to build one. The data structure in Azure Search is primarily an inverted index, which has different characteristics than source data. For an inverted index, size and complexity are determined by content, not necessarily the amount of data you feed into it. A large data source with massive redundancy could result in a smaller index than a smaller dataset containing highly variable content. As such, it's rarely possible to infer index size based on the size of the original data set.
Although estimating future needs for indexes and storage can feel like guesswork, it's worth doing. If a tier's capacity turns out to be too low, you will need to provision a new service at the higher tier and then reload your indexes. There is no in-place upgrade of the same service from one SKU to another.
Step 1: Develop rough estimates using the Free tier
One approach for estimating capacity is to start with the Free tier. Recall that the Free service offers up to 3 indexes, 50 MB of storage, and 2 minutes of indexing time. It can be challenging to estimate a projected index size with these constraints, but the following example illustrates an approach:
- Create a free service
- Prepare a small, representative data set (assume five thousand documents and ten percent sample size)
- Build an initial index and note its size in the portal (assume 30 MB)
Assuming the sample was both representative and ten percent of the entire data source, a 30 MB index becomes approximately 300 MB if all documents are indexed. Armed with this preliminary number, you might double that amount to budget for two indexes (development and production), for a total of 600 MB in storage requirements. This is easily satisfied by the Basic tier, so you would start there.
Step 2: Develop refined estimates using a billable tier
Some customers prefer to start with dedicated resources that can accommodate larger sampling and processing times, and then develop realistic estimates of index quantity, size, and query volumes during development. Initially, a service is provisioned based on a best-guess estimate, and then as the development project matures, teams usually know whether the existing service is over or under capacity for projected production workloads.
Review service limits at each tier to determine whether lower tiers can support the quantity of indexes you need. Across the Basic-S1- S2 tiers, index limits are 15-50-200, respectively.
- Start low, on Basic or S1 if you are at the beginning of your learning curve.
- Start high, at S2 or even S3, if large-scale indexing and query loads are self-evident.
Build an initial index to determine how source data translates to an index. This is the only way to estimate index size.
Monitor storage, service limits, query volume, and latency in the portal. The portal shows you queries per second, throttled queries, and search latency; all of which can help you decide if you are at the right tier. Aside from portal metrics, you can configure deep monitoring, such as clickthrough analysis, by enabling search traffic analytics.
Index number and size are equally relevant to your analysis because maximum limits are reached through full utilization of storage (partitions) or by maximum limits on resources (indexes, indexers, and so forth), whichever comes first. The portal helps you keep track of both, showing current usage and maximum limits side by side on the Overview page.
Storage requirements can be over-inflated if documents contain extraneous data. Ideally, documents contain only the data you need for the search experience. Binary data is non-searchable and should be stored separately (perhaps in an Azure table or blob storage) with a field in the index to hold a URL reference to the external data. The maximum size of an individual document is 16 MB (or less if you are bulk uploading multiple documents in one request). Service limits in Azure Search has more information.
Query volume considerations
Queries-per-second (QPS) is a metric that gains prominence during performance tuning, but is generally not a tier consideration unless you expect high query volume at the outset.
The standard tiers can deliver a balance of replicas to partitions, supporting faster query turnaround through additional replicas for loading balancing and additional partitions for parallel processing. You can tune for performance after the service is provisioned.
Customer who expect strong sustained query volumes from the outset should consider higher tiers, backed by more powerful hardware. You can then take partitions and replicas offline, or even switch to a lower tier service, if those query volumes fail to materialize. For more information on how to calculate query throughput, see Azure Search performance and optimization.
Service level agreements
The Free tier and preview features do not come with service level agreements (SLAs). For all billable tiers, SLAs take effect when you provision sufficient redundancy for your service. Two or more replicas are required for query (read) SLA. Three or more replicas are required for query and indexing (read-write) SLA. The number of partitions is not an SLA consideration.
Tips for tier evaluation
Learn how to build efficient indexes, and which refresh methodologies are the least impactful. We recommend search traffic analytics for the insights gained on query activity.
Allow metrics to build around queries and collect data around usage patterns (queries during business hours, indexing during off-peak hours), and use this data to inform future service provisioning decisions. While not practical at an hourly or daily cadence, you can dynamically adjust partitions and resources to accommodate planned changes in query volumes, or unplanned but sustained changes if levels hold long enough to warrant taking action.
Remember that the only downside of under-provisioning is that you might have to tear down a service if actual requirements are greater than you estimated. To avoid service disruption, you would create a new service in the same subscription at a higher tier and run it side by side until all apps and requests target the new endpoint.
Start with a Free tier and build an initial index using a subset of your data to understand its characteristics. The data structure in Azure Search is an inverted index, where size and complexity of an inverted index is determined by content. Remember that highly redundant content tends to result in a smaller index than highly irregular content. As such, it is content characteristics rather than the size of the data set that determines index storage requirements.
Once you have an initial idea of index size, provision a billable service at one of the tiers discussed in this article, either Basic or a Standard tier. Relax any artificial constraints on data subsets and rebuild your index to include all of the data you actually want to be searchable.
Allocate partitions and replicas as needed to get the performance and scale you require.
If performance and capacity are fine, you are done. Otherwise, re-create a search service at a different tier that more closely aligns with your needs.
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